The present invention relates to the analysis of rules for application to state event information. More particularly, the present invention relates to context sensitive analysis of rules for evaluating the state of a managed resource.
Within the past two decades the development of raw computing power coupled with the proliferation of computer devices has grown at exponential rates. This growth along with the advent of the Internet have led to a new age of accessibility—to other people, other systems, and to information. This boom has also led to some complexity in the systems. The simultaneous explosion of information and integration of technology into everyday life has brought on new demands for how people manage and maintain computer systems.
Additionally, the complexity of these systems and the way they work together has and will create a shortage of skilled IT workers to manage all of the systems. The problem is expected to increase exponentially, just as the dependence on technology has. As access to information becomes omnipresent through PC's, hand-held and wireless devices, the stability of current infrastructure, systems, and data is at an increasingly greater risk to suffer outages and general disrepair.
One new model of computing, termed “autonomic computing,” shifts the fundamental definition of the technology age from one of computing, to that defined by data. The term “autonomic” comes from an analogy to the autonomic central nervous system in the human body, which adjusts too many situations automatically without any external help. Similarly, the way to handle the problem of managing a complex IT infrastructure is to create computer systems and software that can respond to changes in the IT (and ultimately, the business) environment, so the systems can adapt, heal, and protect themselves. In an autonomic environment, components work together, communicating with each other and with high-level management tools. They can manage or control themselves and each other.
Self healing technologies are one of the pillars of autonomic computing and on demand. Self-healing requires detecting problematic operations (either proactively through predictions or otherwise) and then initiating corrective action without disrupting system applications. The first step toward this direction is problem determination. In an autonomic computing environment, problem determination uses an intelligent control loop to collect system information, analyze it by applying problem determination rules, plan appropriate responses, and then make necessary adjustments to resolve problems. Self-healing systems are typically rule driven—rules define what the system should do to diagnose and correct a problem. However, a conventional problem determination system can have hundreds or thousands of rules, and must evaluate many of these rules frequently.